Bayesian Forecasting of Stock Returns on the JSE using Simultaneous Graphical Dynamic Linear Models

ArXiv ID: 2307.08665 “View on arXiv”

Authors: Unknown

Abstract

Cross-series dependencies are crucial in obtaining accurate forecasts when forecasting a multivariate time series. Simultaneous Graphical Dynamic Linear Models (SGDLMs) are Bayesian models that elegantly capture cross-series dependencies. This study forecasts returns of a 40-dimensional time series of stock data from the Johannesburg Stock Exchange (JSE) using SGDLMs. The SGDLM approach involves constructing a customised dynamic linear model (DLM) for each univariate time series. At each time point, the DLMs are recoupled using importance sampling and decoupled using mean-field variational Bayes. Our results suggest that SGDLMs forecast stock data on the JSE accurately and respond to market gyrations effectively.

Keywords: Simultaneous Graphical Dynamic Linear Models, Bayesian models, Importance sampling, Mean-field variational Bayes, Multivariate time series, Stocks (Johannesburg Stock Exchange)

Complexity vs Empirical Score

• Math Complexity: 9.0/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced Bayesian state-space modeling, variational inference, and importance sampling, demanding high mathematical sophistication. It applies the model to 40 stocks from the Johannesburg Stock Exchange with CPU-based implementation, indicating solid empirical validation, though the hardware specifications are modest compared to GPU-accelerated industry standards.

  flowchart TD
    A["Research Goal<br>Forecast JSE Stock Returns<br>using SGDLMs"] --> B["Data Input<br>40-dimensional time series<br>from Johannesburg Stock Exchange"]
    B --> C["Model Construction<br>Customised DLM per<br>univariate series"]
    C --> D{"At each time point"}
    D --> E["Recoupling via<br>Importance Sampling"]
    D --> F["Decoupling via<br>Mean-field Variational Bayes"]
    E & F --> G["Computational Process<br>Bayesian inference<br>capturing cross-series dependencies"]
    G --> H["Key Findings<br>Accurate forecasts<br>Effective response to market gyrations"]